Learning Paths
Three Courses,
One Clear Direction
From first steps in Python to mentored AI development work — each course has a defined scope, a realistic workload, and projects that produce something you can use.
Back to HomeOur Methodology
How These Courses Are Structured
Each Gradienta course begins with a clear syllabus. Topics are introduced in order, with each building on the one before. Lessons are short enough to complete in one sitting — typically 20–40 minutes — so they can fit around work or other commitments.
Practice tasks appear throughout each course, not just at the end. Feedback is written and specific rather than automated. The three paths connect: material from the Starter Path is assumed knowledge in Applied Models, and so on. You're not starting from scratch each time.
Clear scope
Full topic list published before enrolment
Realistic hours
Weekly commitment stated per course
Practical tasks
Work with real data throughout
Progression by design
Courses connect — not repeat
Course 01
Starter Path: Python & Data
Beginner · ฿4,000
A welcoming beginner course that covers Python foundations and introduces working with structured data — both of which underpin most AI and machine learning work. Lessons are short and include practice tasks, so the material is consolidated as you go rather than all at once.
This course is for people who are new to coding and want an honest starting point. It won't assume any prior experience, but it does ask for consistent effort. Around 4–6 hours per week works well for most learners.
- Python syntax, data types, control flow
- Working with pandas DataFrames
- Basic data visualisation
- End-of-course data exploration task
- Email support with instructors included
How It Progresses
Python foundations
Syntax, data structures, writing functions
Working with data
Loading, cleaning, and exploring datasets
Data exploration task
Structured project with written feedback
Course 02
Applied Models Studio
Intermediate · ฿15,300
A hands-on course where you build, train, and evaluate machine learning models on real datasets. The curriculum moves through regression, classification, and model assessment, with worked examples at each stage and written feedback on your submissions.
This path is suited to learners who have basic Python knowledge — either from the Starter Path or equivalent prior experience. It includes a guided portfolio project completed at a pace you can manage alongside other work. Around 6–8 hours per week is a reasonable estimate.
- Regression and classification with scikit-learn
- Model evaluation and metrics interpretation
- Feature engineering basics
- Guided portfolio project with feedback
- Email support with instructors included
How It Progresses
Supervised learning foundations
Core algorithms, training, and evaluation
Working with real datasets
Messy data, preprocessing, feature choices
Portfolio project
End-to-end model from data to evaluation
Course 03
Mentored Growth Programme
Advanced · ฿34,500
An extended programme that pairs structured modules with regular mentorship, direct code reviews, and a small learning community. It covers more advanced AI development topics while giving learners the kind of guidance that online self-study usually lacks.
This path is for committed learners who value feedback from an experienced instructor alongside independent study. Workload and expectations are discussed before enrolment — typically 8–10 hours per week including mentor sessions. The scope is intentionally set out clearly because it's a significant commitment.
- Structured modules on advanced AI topics
- Regular one-to-one mentor sessions
- Written code reviews on submitted work
- Small peer learning group
- Extended portfolio project
How It Progresses
Advanced modules
Deeper AI topics with structured reading and tasks
Mentor sessions & code review
Regular touchpoints with written feedback
Extended portfolio project
Substantial piece of work reviewed by instructor
Choose Your Path
Which Course Is Right for You?
Use this to compare what each path includes. If you're unsure, reaching out and describing your background is often the quickest way to find out.
| Feature | Starter Path ฿4,000 |
Applied Models ฿15,300 |
Growth Programme ฿34,500 |
|---|---|---|---|
| Best for… | Complete beginners | Basic Python users | Committed learners |
| Prior coding needed | None | Basic Python | Python + models |
| Typical weekly hours | 4–6 hrs | 6–8 hrs | 8–10 hrs |
| Portfolio project | Extended | ||
| Written instructor feedback | + code review | ||
| Mentor sessions | |||
| Peer learning group |
Across All Courses
Standards That Apply to Every Path
Data Privacy
Learner data handled in line with Thailand PDPA. No third-party marketing use.
Full Syllabus Published
All topics, outcomes, and time estimates visible before enrolment.
Email Support
Instructor contact by email. Responses within one working day.
Content Reviewed Annually
Courses checked each year to reflect changes in the field.
Pricing
Clear Costs, No Extras
Each price covers the full course — content, feedback, support, and project work — with nothing added on afterwards.
Starter
Starter Path
฿4,000
One-time enrolment
- Python & data foundations
- Practice tasks throughout
- End-of-course project
- Written feedback
- Email support
Most detailed
Applied Models
฿15,300
One-time enrolment
- Supervised ML with scikit-learn
- Worked examples throughout
- Guided portfolio project
- Written feedback on submissions
- Email support
With mentorship
Growth Programme
฿34,500
One-time enrolment
- Advanced AI development modules
- Regular mentor sessions
- Code reviews on all submissions
- Peer learning group access
- Extended portfolio project
Ready to Ask?
Not Sure Which Path Fits? Just Ask.
Describe your background and what you're trying to learn, and we'll point you toward the course that makes most sense — without any pressure to choose the most expensive one.
Get in Touch